High Surface Area Mesoporous Silicon Nanoparticles Prepared via Two-Step Magnesiothermic Reduction for Stoichiometric CO<sub>2</sub> to CH<sub>3</sub>OH Conversion
Bibliographic record
Abstract
Magnesiothermic reduction of silicon oxide can result in the formation of nanostructured, mesoporous elemental silicon (mp-Si), which has been explored in a variety of energy applications such as Li-ion battery anodes, photocatalytic water splitting, CO 2 reduction, drug delivery vehicles, and sensors as well as for gas storage. The physical properties of the resultant mp-Si generated via magnesiothermic reduction, and thus the potential utility, are highly dependent on the specific reduction conditions utilized. Herein, we report a modified magnesiothermic reduction method which allows for the synthesis of high surface area mp-Si nanoparticles. The reaction was initiated at 650 °C and then cooled to a lower temperature to minimize heat-induced morphological damage. The nanoparticles were characterized by using powder X-ray diffraction, scanning and transmission electron microscopies, and N 2 adsorption isotherm measurements. Particles prepared by using two-step annealing with the initial processing condition of 650 °C for 30 min followed by 300 °C for 4 h resulted in crystalline and completely reduced mp-Si with a high specific surface area of 542 ± 18 m 2 /g. mp-Si nanoparticles generated by using these specific parameters were further used for stoichiometric CO 2 conversion to CH 3 OH, and the reaction yields were 2.5 times higher than prior reports, demonstrating usefulness in effecting an important chemical transformation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".